What does this research mean for the field?

Integrating artificial intelligence, 3D printing, and pharmacogenomics into a unified framework can accelerate drug development and enable personalized therapies, provided that current scientific, regulatory, and ethical challenges are addressed. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

This review aims to explore the integration of AI, 3D printing, and pharmacogenomics in drug development, highlighting challenges and opportunities.

May 12, 2026Open Access

AI-driven drug development: integrating computational design, 3D printing, and genomic medicine

Key Points

This review aims to explore the integration of AI, 3D printing, and pharmacogenomics in drug development, highlighting challenges and opportunities.
Narrative review of interactions between AI, 3D printing, and pharmacogenomics in the drug discovery process.
Identification of critical points for further investigation, including clinical trials and validation of AI algorithms.
Proposed integration framework for complementary operations across pharmaceutical lifecycle.
AI-driven approaches enhance drug discovery by targeting identification, molecular candidate generation, and predicting interactions.
Additive manufacturing offers potential for personalized dosage forms despite challenges like thermal degradation and scalability.
Pharmacogenomics highlights the importance of genetic variations in dosing but faces issues like bias in datasets and clinician fatigue.

Abstract

Abstract Artificial intelligence (AI), three-dimensional (3D) printing, and pharmacogenomics are coming together to change the pharmaceutical development, but most of the published reviews treat these technologies in isolation. This narrative review explains how these three domains interact all through the drug discovery-to-delivery process and tries to identify the scientific, regulatory and ethical requirements for their integration. AI-driven approaches will help in accelerating drug discovery by identifying targets, generating molecular candidates, and predicting drug-target interactions. However, less than 20% of identified lead molecules tend to cross Phase I trials, which show that there are many gaps in generalization across different domains. Additive manufacturing helps us develop personalized dosage forms for paediatric doses and orphan disease therapies but the thermal degradation of active pharmaceutical ingredients takes place and limited scalability lead to non-selection. In Pharmacogenomics, the genetic variations is backbone for selection of dosing, but it is affected by bias in genomic datasets, and a phenomenon called clinician alert fatigue. To tackle such problems, the critical points of investigation include head-to-head clinical trials linking 3D-printed outputs to therapeutic outcomes, validation of AI algorithms based on independent datasets, and multi-disciplinary pharmacogenomic datasets. In this review, a new conceptual integration framework is proposed so that we can learn to illustrate how AI, additive manufacturing, and pharmacogenomics can operate by complementing across the pharmaceutical lifecycle. The successful translation of this idea will require collaboration at various levels among discovery scientists, manufacturing engineers, clinicians, and informaticians to deliver personalized therapies within less development timelines for improved patient outcomes, and equal access to all people.

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